Summary of Leveraging Graph Structures to Detect Hallucinations in Large Language Models, by Noa Nonkes et al.
Leveraging Graph Structures to Detect Hallucinations in Large Language Models
by Noa Nonkes, Sergei Agaronian, Evangelos Kanoulas, Roxana Petcu
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method detects hallucinations in large language models by examining the latent space structure and finding associations between generations. It creates a graph that connects closely-lying generations and employs Graph Attention Networks to assign importance based on relevance. The findings show that there is a distinct structure in the latent space, which can be learned and generalized to unseen generations. The method’s robustness is enhanced by incorporating contrastive learning. The model performs similarly without access to search-based methods when evaluated against evidence-based benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used for various tasks like customer support, content creation, and educational tutoring. However, they often generate hallucinations, which can damage trust in the information provided. To solve this issue, a method is proposed that detects hallucinations by looking at the structure of the latent space. It creates a graph to connect similar generations and uses Graph Attention Networks to assign importance based on relevance. The results show that there’s a difference between hallucinated and non-hallucinated generations in the latent space. This method can learn this difference and apply it to new generations. |
Keywords
* Artificial intelligence * Attention * Latent space
